/usr/include/shogun/machine/gp/LogitLikelihood.h is in libshogun-dev 3.2.0-7.5.
This file is owned by root:root, with mode 0o644.
The actual contents of the file can be viewed below.
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* This program is free software; you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation; either version 3 of the License, or
* (at your option) any later version.
*
* Written (W) 2013 Roman Votyakov
*/
#ifndef _LOGITLIKELIHOOD_H_
#define _LOGITLIKELIHOOD_H_
#include <shogun/lib/config.h>
#ifdef HAVE_EIGEN3
#include <shogun/machine/gp/LikelihoodModel.h>
namespace shogun
{
/** @brief Class that models Logit likelihood.
*
* \f[
* p(y|f) = \prod_{i=1}^n \frac{1}{1+exp(-y_i*f_i)}
* \f]
*/
class CLogitLikelihood : public CLikelihoodModel
{
public:
/** default constructor */
CLogitLikelihood();
virtual ~CLogitLikelihood();
/** returns the name of the likelihood model
*
* @return name LogitLikelihood
*/
virtual const char* get_name() const { return "LogitLikelihood"; }
/** returns mean of the predictive marginal \f$p(y_*|X,y,x_*)\f$.
*
* NOTE: if lab equals to NULL, then each \f$y_*\f$ equals to one.
*
* @param mu posterior mean of a Gaussian distribution
* \f$\mathcal{N}(\mu,\sigma^2)\f$, which is an approximation to the
* posterior marginal \f$p(f_*|X,y,x_*)\f$
* @param s2 posterior variance of a Gaussian distribution
* \f$\mathcal{N}(\mu,\sigma^2)\f$, which is an approximation to the
* posterior marginal \f$p(f_*|X,y,x_*)\f$
* @param lab labels \f$y_*\f$
*
* @return final means evaluated by likelihood function
*/
virtual SGVector<float64_t> get_predictive_means(SGVector<float64_t> mu,
SGVector<float64_t> s2, const CLabels* lab=NULL) const;
/** returns variance of the predictive marginal \f$p(y_*|X,y,x_*)\f$.
*
* NOTE: if lab equals to NULL, then each \f$y_*\f$ equals to one.
*
* @param mu posterior mean of a Gaussian distribution
* \f$\mathcal{N}(\mu,\sigma^2)\f$, which is an approximation to the
* posterior marginal \f$p(f_*|X,y,x_*)\f$
* @param s2 posterior variance of a Gaussian distribution
* \f$\mathcal{N}(\mu,\sigma^2)\f$, which is an approximation to the
* posterior marginal \f$p(f_*|X,y,x_*)\f$
* @param lab labels \f$y_*\f$
*
* @return final variances evaluated by likelihood function
*/
virtual SGVector<float64_t> get_predictive_variances(SGVector<float64_t> mu,
SGVector<float64_t> s2, const CLabels* lab=NULL) const;
/** get model type
*
* @return model type Logit
*/
virtual ELikelihoodModelType get_model_type() const { return LT_LOGIT; }
/** returns the logarithm of the point-wise likelihood \f$log(p(y_i|f_i))\f$
* for each label \f$y_i\f$.
*
* One can evaluate log-likelihood like: \f$log(p(y|f)) = \sum_{i=1}^{n}
* log(p(y_i|f_i))\f$
*
* @param lab labels \f$y_i\f$
* @param func values of the function \f$f_i\f$
*
* @return logarithm of the point-wise likelihood
*/
virtual SGVector<float64_t> get_log_probability_f(const CLabels* lab,
SGVector<float64_t> func) const;
/** get derivative of log likelihood \f$log(P(y|f))\f$ with respect to
* function location \f$f\f$
*
* @param lab labels used
* @param func function location
* @param i index, choices are 1, 2, and 3 for first, second, and third
* derivatives respectively
*
* @return derivative
*/
virtual SGVector<float64_t> get_log_probability_derivative_f(
const CLabels* lab, SGVector<float64_t> func, index_t i) const;
/** returns the zeroth moment of a given (unnormalized) probability
* distribution:
*
* \f[
* log(Z_i) = log\left(\int p(y_i|f_i) \mathcal{N}(f_i|\mu,\sigma^2)
* df_i\right)
* \f]
*
* for each \f$f_i\f$.
*
* @param mu mean of the \f$\mathcal{N}(f_i|\mu,\sigma^2)\f$
* @param s2 variance of the \f$\mathcal{N}(f_i|\mu,\sigma^2)\f$
* @param lab labels \f$y_i\f$
*
* @return log zeroth moments \f$log(Z_i)\f$
*/
virtual SGVector<float64_t> get_log_zeroth_moments(SGVector<float64_t> mu,
SGVector<float64_t> s2, const CLabels* lab) const;
/** returns the first moment of a given (unnormalized) probability
* distribution \f$q(f_i) = Z_i^-1
* p(y_i|f_i)\mathcal{N}(f_i|\mu,\sigma^2)\f$, where \f$ Z_i=\int
* p(y_i|f_i)\mathcal{N}(f_i|\mu,\sigma^2) df_i\f$.
*
* This method is useful for EP local likelihood approximation.
*
* @param mu mean of the \f$\mathcal{N}(f_i|\mu,\sigma^2)\f$
* @param s2 variance of the \f$\mathcal{N}(f_i|\mu,\sigma^2)\f$
* @param lab labels \f$y_i\f$
* @param i index i
*
* @return first moment of \f$q(f_i)\f$
*/
virtual float64_t get_first_moment(SGVector<float64_t> mu,
SGVector<float64_t> s2, const CLabels* lab, index_t i) const;
/** returns the second moment of a given (unnormalized) probability
* distribution \f$q(f_i) = Z_i^-1
* p(y_i|f_i)\mathcal{N}(f_i|\mu,\sigma^2)\f$, where \f$ Z_i=\int
* p(y_i|f_i)\mathcal{N}(f_i|\mu,\sigma^2) df_i\f$.
*
* This method is useful for EP local likelihood approximation.
*
* @param mu mean of the \f$\mathcal{N}(f_i|\mu,\sigma^2)\f$
* @param s2 variance of the \f$\mathcal{N}(f_i|\mu,\sigma^2)\f$
* @param lab labels \f$y_i\f$
* @param i index i
*
* @return the second moment of \f$q(f_i)\f$
*/
virtual float64_t get_second_moment(SGVector<float64_t> mu,
SGVector<float64_t> s2, const CLabels* lab, index_t i) const;
/** return whether logit likelihood function supports binary classification
*
* @return true
*/
virtual bool supports_binary() const { return true; }
};
}
#endif /* HAVE_EIGEN3 */
#endif /* _LOGITLIKELIHOOD_H_ */
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